Method and communication device for interference concellation in a cellular tdma communication system

ABSTRACT

A system and method for interference cancellation of received data via a communication channel in a cellular communication system having corresponding channel impulse response coefficients. Linear filtering of the received data is performed and thereafter a non-linear detection is executed to get detected data by non-linear signal processing. Filter coefficients are determined for linear pre-filtering of the received data to suppress non-Gaussian interference. The pre-filtered data is further processed by non-linear detection to get detected data.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to a method for interference cancellation in acellular TDMA (Time Division Multiple Access) communication systemaccording to pre-characterizing part of claim 1. Further, the inventionrelates to a communication device for interference cancellation in acellular TDMA communication system according to pre-characterizing partof claim 14.

2. Description of the Related Art

Currently, single antenna co-channel interference cancellation (SAIC) isbeing a hot topic, especially for the Global System for Mobilecommunications/Enhanced Data (GSM/EDGE) downlink. In field trails,tremendous capacity gains have recently been demonstrated [1] 3GAmericas, “SAIC and synchronized networks for increased GSM capacity,”www.3gamericas.org, September 2003, particularly for synchronizednetworks in urban areas. As a consequence, the upcoming GSM/EDGE releasewill be tightened with respect to interference cancellation.

One of the most challenging tasks is the design of the interferencecanceller, especially if due to cost, volume, power consumption, anddesign aspects only one receive antenna is available. Most interferencecancellers fail if the number of receive antennas do not exceed thenumber of co-channels [2]J. H. Winters, “Optimum combining in digitalmobile radio with co-channel interference,” IEEE J. Sel. Areas Commun.vol. 2, no. 4, pp. 528-539, 1984, [3] A. M. Kuzminskiy and D.Hatzinakos, “Semiblind estimation of spatio-temporal filter coefficientsbased on a training-like approach,” IEEE signal Processing Letters, vol.5, no. 9, pp. 231-233, September 1998, [4]A. M. Kuzminskiy, C. Luschi,and P. Strauch, “Comparison of linear and MLSE-spatio-temporalinterference rejection combining with an antenna array in a GSM System,”in Proc. IEEE Veh. Techn. Conf. '00, Tokyo, Japan, pp. 172-176, May2000.

A receiver structure with linear filtering and decoupled non-lineardetection has been studied in [5] D. Giancola, U. Girola, S. Parolari,A. Picciriello, U. Spagnolini, and D. Vincenzoni, “Space-time processingfor time varying co-channel interference rejection and channelestimation in GSM/DCS systems,” in Proc. IEEE Veh. Techn. Conf. '99, May1999, [6] S. Arnyavistakul, J. H. Winters, and N. R. Sollenberger,“Joint equalization and interference suppression for high data ratewireless systems,” IEEE J. Sel. Areas Commun. vol. 18, no. 7, pp.1214-1220, July 2000, [7] R. Friedman and Y. Bar-Ness, “Combinedchannel-modified adaptive array MMSE canceller and Viterbi equalizer,”in Proc. IEEE Veh. Techn. Conf. '01, pp. 209-213, 2001, [8] A. L. F. deAlmeida, C. M. Panazio, F. R. P. Cavalcanti, and C. E. R. Fernandes,“Space-time processing with a decoupled delayed decision-feedbacksequence estimator,” in Proc. IEEE Veh. Techn. Conf. '02, Birmingham,Ala., USA, May 2002, [9] K. Kim and G. L. Stüber, “Interferencecancelling receiver for range extended reception in TDMA cellularsystems,” in Proc. IEEE Veh. Techn. Conf. '03, October 2003 usingmultiple receive antennas. A special case of linear filtering inconjunction with a memory-less detector has been investigated in [10] B.R. Petersen and D. D. Falconer, “Suppression of adjacent-channel,co-channel, and intersymbol interference by equalizers and linearcombiners,” IEEE Trans. Commun. vol. 42, no. 12, pp. 3109-3118, December1994, [11] H. Trigui and D. T. M. Slock, “Cochannel interferencecancellation within the current GSM standard,” in Proc. IEEE ICUPC '98,pp. 511-515, 1998, [3], and [4] for the case of multiple receiveantennas. Further, there is known Kammeyer's pre-filter [12] K. D.Kammeyer, “Time truncation of channel impulse responses by linearfiltering: A method to reduce the complexity of Viterbi equalization,”AEÜ, vol. 48, no. 5, pp. 237-243, 1994, which is designed in order totruncated a single InterSymbol Interference (ISI) channel, but whichdoes not take Co-Channel Interference (CCI) into account. A similarpre-filter has also been proposed by Al-Dhahir and Cioffi [13] N.Al-Dhahir and J. M. Cioffi, “MMSE decision feedback equalizers:Finite-length results,” IEEE Trans. Inform. Theory, vol. 41, no. 4, pp.961-975, July 1995, for the case of Gaussian noise plus interference. Inthe present case, focus shall be on a few numbers of dominantinterferers, which is the typical case in cellular networks, i.e., onnon-Gaussian interference. [14] R. Meyer, R. Schober, and W. Gerstacker,EP 1221780 A1, published July 2002 discloses a pre-filter withreal-valued processing, being designed according to the principlesestablished in [11].

OBJECT OF THE INVENTION

An object of the invention is to improve a method and a communicationdevice for interference cancellation in a cellular TDMA communicationsystem.

SUMMARY OF THE INVENTION

This object is solved by a method for interference cancellation in acellular TDMA (Time Division Multiple Access) communication systemaccording to the features of claim 1. Further, the object is solved by acommunication device for interference cancellation in a cellular TDMAcommunication system according to the features of claim 14.

According to the preferred embodiment there is provided a method forinterference cancellation of received data being received via acommunication channel in a cellular communication system and havingcorresponding channel coefficients, especially channel impulse responsecoefficients, said method comprising the steps of linear filtering ofsaid received data, and thereafter of executing a detection, especiallynon-linear detection to get detected data by especially non-linearsignal processing. Further, there are determined filter coefficients forlinear pre-filtering of said received data to suppress non-Gaussianinterference when filtering said received data.

Further, according to the preferred embodiment there is provided acommunication device for communicating with another device in a cellularcommunication network, said communication device comprising a receiverunit for receiving of data sent from said other device via acommunication channel and receiving data sent from at least one furtherdevice via a disturbing, especially via an interfering channel, saidchannels having corresponding channel coefficients, and at least oneprocessing unit for interference cancellation of said received data andfor linear filtering of said received data and, thereafter, fordetection, especially non-linear detection to get detected data byespecially non-linear signal processing. Further, at least one of saidat least one processing units being designed for determining of filtercoefficients for linear pre-filtering of said received data to suppressnon-Gaussian interference when filtering said received data, whereinsaid processing unit is designed especially for performing a methodaccording to any of preceding claims.

Advantageous embodiments are subject matter of dependent claims.

Advantageously, there is provided a method, comprising the step ofprocessing of pre-filtered data by non-linear detection to get detecteddata, especially to get detected symbols.

Advantageously, there is provided a method comprising the steps ofdetermining of optimized channel coefficients and using these optimizedchannel coefficients as coefficients in a decision-feedback equalizationto get detected data. Such a method is preferred when performing saiddecision-feedback equalization after the step of pre-filtering of data.Further, such a method is preferred when said optimized channelcoefficients being determined from said filter coefficients and saidchannel coefficients, especially being determined as a convolutionbetween said filter coefficients and said channel coefficients.

Advantageously, there is provided a method claim, comprising the stepsof performing of an auto-correlation of said received data and across-correlation between a sequence of said received data and asequence of known symbols for said determining of said filtercoefficients. Especially, such method is preferred when said sequence ofknown symbols being part of sent data corresponding to a part of saidreceived data or being determined from said received data.

According to a first sub case there is preferred a method comprising thestep of calculation of an auto-correlation matrix using knowledge of atleast one desired channel transmitting desired data and using knowledgeof at least one disturbing, especially interfering channel, transmittingdisturbing data, said knowledge being determined by channel estimation.

According to a second sub case there is preferred a method comprisingthe step of calculation of an auto-correlation matrix using onlyknowledge of at least one desired channel transmitting desired data,said knowledge being determined by channel estimation.

According to a third sub-case there is preferred a method comprising thestep of calculation of an auto-correlation matrix using the receivedsamples, rather than the knowledge of a desired channel transmittingdesired data or the knowledge of a disturbing, especially interferingchannel, transmitting disturbing data.

Advantageously, there is provided a method comprising the step ofdetermining the optimized channel coefficients for a non-linearequalizer by convolution of the filter coefficients with the originallyestimated channel coefficients of the desired data.

Advantageously, there is provided a method comprising the step ofestimating optimized channel coefficients for non-linear equalizationusing the pre-filtered signal and using channel estimation.

Advantageously, there is provided a method comprising the step ofprocessing of the pre-filtered data using a memory-less detector, usinga backwardly directed filter of a MMSE-DEE (MMSE-DFE: Minimum MeanSquare Error-Decision-Feedback Equalizer) in connection with amemory-less detector, or using a non-linear equalizer.

Preferred is a communication device and corresponding method, whereinsaid at least one processing unit being designed as a linear pre-filterto suppress non-Gaussian interference followed by a non-linear detectorto cancel intersymbol interference.

Especially, a receiver structure is preferred being suitable forsingle-antenna co-channel interference cancellation in cellular TDMAnetworks. The receiver consists of a novel linear pre-filter followed bya non-linear detector. The task of the pre-filter is to suppressnon-Gaussian interference, especially to suppress co-channelinterference, whereas the task of the non-linear detector is to cancelintersymbol interference. The pre-filter is designed according to theprinciple of minimum mean square error decision-feedback equalization,without solving an eigenvalue problem. The non-linear detector may be anarbitrary non-linear equalizer, in the simplest case just a memory-lessdetector. The receiver structure is compatible with conventional TDMAreceivers ignoring co-channel interference. Receiver and correspondingmethod are applicable in cellular communication systems, e.g. like GSM.

Especially, focus is on a receiver structure, where Co-ChannelInterference (CCI) reduction is separated from InterSymbol Interference(ISI) suppression. CCI reduction is done by means of a time-domainlinear filter, followed by a non-linear detector in order to perform ISIsuppression of the desired users channel. For example, non-lineardetector is constructed as a trellis-based, tree-based, graph-basedequalizer or as a feedback filter of a decision-feedback equalizer.Besides interference suppression, the linear filter is able to truncatethe overall channel impulse response seen by the subsequent non-lineardetector. Advantageously, this receiver structure is compatible withconventional TDMA (Time Division Multiple Access) receivers ignoringCCI.

The method and the receiver structure are advantageously in somerespects. A linear co-channel interference canceller is typically lesssensitive with respect to model mismatch compared to a multi-user-basedco-channel interference canceller. Model mismatch may be due toasynchronous interference, non-perfect channel estimation, EDGEinterferer in a GSM environment or vice versa, etc.

Besides interference suppression, the linear filter is able to truncatethe overall channel impulse response seen by the decoupled non-lineardetector. The memory length of the overall channel impulse response is adesign parameter.

Since the non-linear detector is matched to the possibly truncateddesired users channel only, it is much simpler than a multi-user-basedco-channel interference canceller.

In contrast to a similar receiver structure in [5], [6], [7], [8], [9]using multiple receive antennas, focus of preferred embodiment is onsingle-antenna interference cancellation, which is more demanding thanspace-time processing. Still, multiple antennas are optional accordingto design of preferred embodiment. The preferred pre-filter may beinterpreted as a generalization of Kammeyer's pre-filter [12], which isdesigned in order to truncated a single ISI channel, but which does nottake CCI into account. According to preferred embodiment focus is on afew number of dominant interferer, which is the typical case in cellularnetworks, i.e., on non-Gaussian interference, especially on co-channelinterference.

BRIEF DESCRIPTION OF THE DRAWINGS

An embodiment will be described hereinafter in further detail withreference to drawings.

FIG. 1 illustrates a cellular communication network and a mobile stationreceiving data from a base station of a first cell and receivingco-channel interfering signals from a second base station;

FIG. 2 illustrates components of the mobile station used for sequenceestimation of received data;

FIG. 3 illustrates a flow chart of a preferred method for performingdecoupled linear filtering and non-linear detection;

FIG. 4 illustrates a decoupled linear filtering and non-linear detectionusing in a simple case a memory-less detector as non-linear detector;

FIG. 5 illustrates a fractionally-spaced Minimum Mean Square Error(MMSE) decision-feedback equalizer;

FIG. 6 illustrates a raw Bit Error Rate (BER) of desired user versusChannel Interference (C/I) using TU50 channel model of GSM, joint LeastSquare (LS) channel estimation, and a synchronous GSM network;

FIG. 7 illustrates a raw BER of desired user versus C/I using a TU50channel model, joint LS channel estimation, and a synchronous GSMnetwork;

FIG. 8 illustrates a raw BER of desired user versus C/I using a TU0channel model of GSM, perfect channel knowledge, and a synchronous GSMnetwork;

FIG. 9 illustrates a raw BER of desired user versus C/I using a TU50channel model, LS channel estimation for desired user, and a synchronousGSM network;

FIG. 10 illustrates a raw BER of desired user versus C/I using a TU50channel model, no channel estimation, and a synchronous GSM network; and

FIG. 11 illustrates a raw BER of desired user versus C/I using a TU50channel model, no channel estimation, and an asynchronous GSM network.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The preferred embodiment relates to a method and communication devicefor Single Antenna Interference Cancellation (SAIC) for cellular TDMAnetworks by means of decoupled linear filtering and especiallynon-linear detection.

As can be seen from FIG. 1 a mobile station MS used as a user stationcommunicates via a radio channel with a first base station BS1 of acellular communication network GSM. The mobile station stays in a firstradio cell c1 around the first base station BS1. There are some objectsO disturbing communication between the mobile station MS and the basestation BS1. A first object O interrupts direct channel path s1 betweenthe first base station BS1 and the mobile station MS. The second objectO arranged beside the direct communication path serves as a reflectorfor radio waves. Therefore, a second communication path s2 transmitsradio waves sent by the first base station BS1 and being reflected bythe second object O. Therefore, mobile station receives first data via afirst communication path s1 and second data via a second communicationpath s2. Signal strength of data received via different communicationpaths s1, s2 is different one from each other. Further, data receivedover second communication path s2 are received to a later time than datareceived via direct first communication path s1.

The communication network provides further base stations BS2, BS3 eachhaving a communication cell c2, c3, said communication cells c1-c3 beingarranged in an overlapping manner. A mobile station MS1 moving from afirst cell c1 to a second cell c2 changes from a first base station BS1to a second base station BS2 after reaching handover regions ho of theoverlapping cells. However, before reaching a handover region ho themobile station MS receives interfering signals i11, i12, i21 from theother base stations BS2, BS3. Especially, the interfering signals aresignals sent from the other base stations BS2, BS3 on the same channelto further mobile stations MS*, MS′ in their communication cells c2, c3.Therefore, the mobile station receives co-channel interferencedisturbing the data received from their own first base station BS1. Inaddition, the mobile station MS1 receives noise n, especially whiteGaussian noise. Usually, the base stations BS1-BS3 of a cellularcommunication network are connected via a base station controller BSC toother components of the communication network. To avoid co-channelinterference base station controller BSC assigns different channels toneighboring cells c1-c3. According to a preferred embodiment samechannels shall be used in neighboring cells. In the following it isproposed to use sequence estimation, in the case of GSM adelayed-decision feedback sequence estimation to improve reconstructionof data received by the mobile station MS1 to reduce co-channelinterference.

Data y(k) received by the mobile station MS can be written as:$\begin{matrix}{{{y\lbrack k\rbrack} = {{\sum\limits_{l = 0}^{L}{{h_{l}\lbrack k\rbrack}{a\left\lbrack {k - l} \right\rbrack}}} + {\sum\limits_{j = 1}^{J}{\sum\limits_{l = 0}^{L}{{g_{j,i}\lbrack k\rbrack}{b_{j}\left\lbrack {k - l} \right\rbrack}}}} + {n\lbrack k\rbrack}}},{0 \leq k \leq {K - 1.}}} & (1)\end{matrix}$

The first terms describe the signals s1, s2, . . . received viacommunication paths i.e. communication channels s1, s2 from the firstbase station BS1. These signal components s1, s2 shall be used bysequence estimation to reconstruct data an originally sent by the firstbase station BS1. For performing this sequence estimation a channelimpulse response h₁ is used. The channel impulse response h₁ can bedetermined from received data or from received special symbols, e.g. atraining sequence.

Second sums and terms correspond to the signal components received viainterfering communication paths i11, i12, i21 from second and third basestations BS2, BS3. These interfering signal components i11, i12, i21 andin addition further noise components n(k) has to be distinguished by thesequence estimation procedure. Especially, the signal components s1, s2and the interfering signals components i11, i12, i21 has been sent asinterfering data b_(j) by corresponding base stations BS1, BS2, BS3using a co-channel especially using same frequencyf(s_(l))=f(i_(j,l))−f(n). For performing this sequence estimation achannel impulse response g_(j,l) is used. The channel impulse responseg_(j,l) can be determined from received data or from received specialsymbols, e.g. a training sequence.

As can be seen from FIG. 2, mobile station MS comprises a plurality ofcomponents. A transmitter or at least a receiver unit TX/RX receives thesignal y⁽¹⁾(k). A processing unit C serves for operating of the mobilestation MS and for running processes for digital signal processing. Onecomponent of the processor unit C is a viterbi decoder VIT. Shownviterbi decoder VIT uses K=4 states for data processing. Further, mobilestation MS comprises a memory M for storing data y to be processed, dataa being processed and procedures and programs for processing of thedata. Further, mobile user station MS comprises a fractionally-spacedMinimum Mean Square Error Decision-Feedback Equalizer MMSE DFE forpre-filtering of received data y and estimation of data a. Components ofthe mobile station MS are connected by a data bus B.

As can be seen from FIG. 2 the mobile user station MS receives areceived signal y(k) comprising several channel impulse responsecomponents. Compared with FIG. 1 the channel impulse response hcomprises a first signal component having an intermediate intensity andoriginating from direct signal path s1 of desired data a sent from firstbase station BS1. Thereafter, at a later time t, user station MSreceives a response channel component via second signal path s2 fromdesired data sent from first base station BS1. As a possible exceptionof regular intensity, the signal intensity of second channel impulseresponse is higher than that of first component because there is anobject O within the direct signal path s1 reducing signal strength.Further, channel impulse response components might come from second andthird base stations BS2, BS3 i.e. these are co-channel interferingcomponents. Exemplarily shown channel impulse response h is unfavorablefor using as channel coefficients in decision-feedback equalizer DFE.

According to the preferred embodiment there are determined, especiallycomputed filter coefficients ŵ. As can be seen from FIG. 5 the filtercoefficients ŵ are used for filtering the components of the receivedsignal y in such a way, that pre-filtered data z[k] are optimized forfurther signal processing. These pre-filtered data z[k] can be useddirectly for further signal processing in a non-linear detector VIT.Further, the pre-filtered data z[k] can be processed by adecision-feedback equalizer DFE. After estimation detected data, i.e.especially estimated data a[k−k₀] can be used for further signalprocessing and can be delivered to e.g. a viterbi decoder VIT. Detecteddata correspond to transmitted data as good as possible and can becalled as transmitted data, too.

Further, the estimated data a[k−k₀] are used in decision-feedbackequalizer DFE for further processing together with optimized channelimpulse response {tilde over (h)}=({tilde over (h)}_(1,w). . . , {tildeover (h)}_(L,b)). The estimated data are delayed up to L times bydelaying units z⁻¹. The estimated data a being delayed one time areprocessed with first component {tilde over (h)}_(1,w), estimated data abeing delayed two times are processed with second component of optimizedchannel impulse response {tilde over (h)}, and so on. All in such a wayprocessed data are added. Added data are subtracted from nextpre-filtered data value z[k] before next estimation step.

Summarized, as can be seen from FIGS. 4 and 5, the antenna receivedsignal r(t) is pre-processed by a front-end unit to get the receivedsignal y having unfavorable channel impulse response h. By minimizing acost function C there can be get determined, especially computed filtercoefficients ŵ from unfavorable channel impulse response h. Thereafter,received data y are filtered with computed filter coefficients ŵ being alinear pre-filter. Thereafter, pre-filtered data z[k] are processed by anon-linear detector. The non-linear detector uses an optimized channelimpulse response {tilde over (h)}, said optimized channel impulseresponse {tilde over (h)} being calculated especially from receivedsignal y and the pre-filter coefficients ŵ.

As can be seen from FIG. 3, there are received data having unfavorabledistribution of channel impulse response h in a beginning step S0.Thereafter, filter coefficients w are computed using e.g. anauto-correlation and a cross-correlation of received signal y and knownsymbols in a first processing step S1. Thereafter, in a secondprocessing step S2 there is performed a convolution y_(w):=ŵ^(h)y to getpre-filtered data z. In a third processing step S3 there is performed aLS (Least Square) channel estimation in especially conventional mannerof the convoluted signal to get estimated symbols a. In a fourth step S4there is performed a minimum Least Square estimation MLSE usingconvoluted data y_(w) and the estimated symbols a. After processing ofall received data process ends S5.

In the following there is offered an introduction of the equivalentdiscrete-time channel model under consideration, a short description ofthe conventional design rule for implementing a linear filter accordingto [5], [7], [8]. Then, a design rule according to preferred embodimentfor implementing the linear filter in the presence of non-Gaussianinterference is presented, which is much simpler and which leads tofurther insights. In particular, three different options for trainingthe filter are presented. Finally, numerical results are presented andconclusions are drawn.

The equivalent discrete-time channel model considered is given as$\begin{matrix}{{{y^{(i)}\lbrack k\rbrack} = {{\sum\limits_{l = 0}^{L}{{h_{l}^{(i)}\lbrack k\rbrack}{a\left\lbrack {k - l} \right\rbrack}}} + {\sum\limits_{j = 1}^{J}{\sum\limits_{l = 0}^{L}{{g_{j,l}^{(i)}\lbrack k\rbrack}{b_{j}\left\lbrack {k - l} \right\rbrack}}}} + {n^{(i)}\lbrack k\rbrack}}},{0 \leq k \leq {K - 1.}}} & (1)\end{matrix}$where y^((i))[k]εC is a k-th baud-rate output sample of an i-thpolyphase channel, L is an effective memory length of the discrete-timeISI channel model, h_(l) ^((i))[k]εC are the channel coefficients of thei-th polyphase channel of a desired user MS (E{∥h^((i))[k]²∥}=1),g_(j,l) ^((i))[k]εC are the channel coefficients of the i-th polyphasechannel of the j-th interferer, J is the number of interferer, a[k] andb_(j)[k] is a k-th independent identically distributed (i.i.d.) datasymbol of the desired user and the j-th interferer, respectively, bothrandomly drawn over an M-ary alphabet (E{a[k]}=E{b_(j)[k]}=0,E{a[k]²}=E{b_(j)[k]²}=1), n^((i))[k]εC is the k-th sample of a Gaussiannoise process (E{n^((i))[k]}=0, E{n^((i))[k]²}=N₀/E_(s)), k is a timeindex, and K is a number of M-ary data symbols per burst. All randomprocesses are assumed to be mutually independent. If the signalbandwidth does not exceed twice the Nyquist bandwidth, two samples persymbol are sufficient. This scenario is assumed in the following. Thecorresponding polyphase channels are labeled i=1 and i=2, respectively.The channel coefficients h^((i))[k]:=[h₀ ^((i))[k], . . . , h_(L)^((i))[k]]^(T) and g_(j) ^((i))[k]:=[g_(j,0) ^((i))[k], . . . , g_(j,1)^((i))[k]]^(T) comprise pulse shaping, the respective physical channel,analog receive filtering, the sampling phase, and the samplingfrequency. Without loss of generality, the effective memory length, L,is assumed to be the same for all co-channels. Some coefficients may bezero. In case of square-root Nyquist receive filtering and baud-ratesampling, the Gaussian noise processes of all polyphase channels arewhite. This case is assumed in the following. The equivalentdiscrete-time channel model is suitable both for synchronous as well asasynchronous TDMA networks, because time-varying channel coefficientsare considered.

Conventional design of the linear filter is presented to highlightdifferences and to introduce design according to preferred embodiment.For simplicity, a synchronous TDMA network and low speeds of stations MSare assumed in the following for the purpose of filter design. Inconjunction with frequency hopping, the second assumption corresponds toa so-called block fading channel model. Correspondingly, the time indexk can be dropped for convenience. In case of asynchronous bursts, thechannel coefficients are only piece-wise constant, i.e., the time indexcannot be dropped.

As a design criterion for the linear filter, in exemplarity amaximization of the Signal to Interference plus Noise Ratio (SINR) atthe output of the linear filter is chosen: $\begin{matrix}{{{Max} - {SINR}}:={\max\limits_{\overset{\sim}{w},{\overset{\sim}{h}}_{w}}\frac{E\left\{ {{{\overset{\sim}{w}}^{H}y}}^{2} \right\}}{E\left\{ {{{{\overset{\sim}{w}}^{H}y} - {{\overset{\sim}{h}}_{w}^{H}a}}}^{2} \right\}}}} & (2)\end{matrix}$where the maximization is jointly with respect to the filtercoefficients, {tilde over (w)}, and the overall channel coefficientsincluding filtering of the desired user, {tilde over (h)}_(w).

The filter coefficients can be written in vector form as {tilde over(w)}=[{tilde over (w)}^((1)T),{tilde over (w)}^((2)T)]^(T), where thefilter coefficients of each polyphase channel are given by {tilde over(w)}^((i))=[{tilde over (w)}₀ ^((i)), {tilde over (w)}₁ ^((i)), . . .{tilde over (w)}_(L) _(w) ^((i))]^(T) and L_(w) is the filter order ofeach polyphase channel. Without loss of generality, oversampling by afactor of two is assumed, i.e., iε{1,2}.

Note that further oversampling does not lead to any performanceimprovement in contrast to increasing the number of receive antennas.Specifically, the degree of diversity is not further enhanced.

The output samples of the analog receive filter can be written in vectorform as y=[y^((1)T),y^((2)T)]^(T), where y^((i))=[k^((i))[k],y^((i))[k−1], . . . , y^((i))[k−L_(w)]]^(T) and 0≦k≦K−1.

Hypotheses of the overall channel coefficients including filtering ofthe desired user are denoted as {tilde over (h)}_(w)=[{tilde over(h)}_(0,w), {tilde over (h)}_(1,w), . . . {tilde over (h)}_(L′,w)]^(T),where L′ is the effective memory length. Note that {tilde over (h)}_(w)is symbol spaced. If the filter length L_(w) is sufficiently large, thefilter truncates or is capable to truncate the overall impulse response,i.e., L′≦L. As discussed when describing preferred embodiment, for awell-designed filter in conjunction with a suitable non-linear detectorsuch as a trellis-based, tree-based, or graph-based equalizer, L′ is adesign parameter. It can be interpreted as the desired memory length ofthe overall impulse response.

The corresponding data vector is given by a=[a[k−k₀], a[k−k₀−1], . . . ,a[k−k₀−L′]]^(T), where k₀ is the decision delay of the linear filter,which is often chosen as k₀=(L_(w)+1)/2

Recall that the coefficients of the linear filter, {tilde over (w)}, andthe channel coefficients, {tilde over (h)}_(w), are jointly optimized.The optimum filter and channel coefficients with respect to the maximumSINR are denoted as ŵ and ĥ_(w), respectively. According to thepreferred embodiment these optimum filter and channel coefficients ŵ andĥ_(w), respectively, has to be determined.

A maximization of (2) corresponds to a minimization of the cost functionC:=E{{tilde over (w)} ^(H) y−{tilde over (h)} _(w) ^(H) a| ²},that is $\begin{matrix}{C_{\min}:={{\min\limits_{\overset{\sim}{w},{\overset{\sim}{h}}_{w}}\left\{ C \right\}} = {\min\limits_{\overset{\sim}{w},{\overset{\sim}{h}}_{w}}{E{\left\{ {\left( {{{\overset{\sim}{w}}^{H}y} - {{\overset{\sim}{h}}_{w}^{H}a}} \right)\left( {{{\overset{\sim}{w}}^{H}y} - {{\overset{\sim}{h}}_{w}^{H}a}} \right)^{H}} \right\}.}}}}} & (4)\end{matrix}$

The minimum of C is obtained when the following conditions apply:$\begin{matrix}{\frac{\partial C}{\partial\overset{\sim}{w}} = {{0^{T}{\quad\quad}{and}\quad\frac{\partial^{2}C}{\partial{\overset{\sim}{w}}^{2}}} \geq 0.}} & (5)\end{matrix}$

By using the Wirtinger derivative of the cost function C we obtain$\begin{matrix}{\frac{\partial C}{\partial\overset{\sim}{w}} = {{{\overset{\sim}{w}}^{H}R_{yy}} - {{\overset{\sim}{h}}_{w}^{H}{R_{ya}.}}}} & (6)\end{matrix}$

R_(yy):=E{yy^(H)} denotes the 2(L_(w)+1)×2(L_(w)+1) auto-correlationmatrix of the received samples and R_(ya):=E{ay^(H)} denotes the(L′+1)×2(L_(w)+1) cross-correlation matrix between the data sequence andthe received samples. Combining (5) and (6), there is obtained theoptimum filter coefficients minimizing the cost function C asŵ ^(H) =ĥ _(w) ^(H) R _(ya) R _(yy) ⁻¹.  (7)

By substituting (7) into (4), there is obtainedC _(min) =ĥ _(w) ^(H) [R _(aa) −R _(ya) R _(yy) ⁻¹ R _(ya) ^(H) ]ĥ_(w),  (8)where R_(aa):=E{aa^(H)}=I is the (L′+1)×(L′+1) auto-correlation matrixof the data symbols a[k], which is an identity matrix due to theassumption of i.i.d. data, 0≦k≦K−1.

According to conventional concept, equation (8) leads to an eigenvalueproblem:[−R _(ya) R _(yy) ⁻¹ R _(ya) ^(H) ]ĥ _(w)=λ_(min) ĥ _(w),  (9)where λ_(min) is the smallest eigenvalue of the matrix [I−R_(ya)R_(yy)⁻¹R_(ya) ^(H)]. The optimum channel coefficient vector ĥ_(w) isequivalent to the eigenvector corresponding to the smallest eigenvalueλ_(min) of the matrix [I−R_(ya)R_(yy) ⁻¹R_(ya) ^(H)]. To eliminate thetrivial result ĥ_(w)=0, a certain constraint has to be defined. Often,the overall impulse response is (i) chosen to be monic, i.e., ĥ_(0,w)=1,or (ii) normalized according to ∥ĥ_(w)∥²=1. In coded systems it may bemore suitable, however, to use a linear filter with amplification one.

An insertion of the channel coefficients ĥ_(w) in (7) gives the optimumfilter coefficients ŵ. Hence, traditionally, in the first step thechannel coefficients and in the second step the filter coefficients arecomputed. The symbol-spaced channel coefficients ĥ_(w) are provided tothe decoupled non-linear detector.

The main problem of the conventional filter design discussed so far isthe computational complexity. It appears to be very difficult to solvethe eigenvalue problem (9), even by means of a Cholesky factorization asproposed in [5].

According to the preferred embodiment, in the following there ispresented as an alternative another filter design, which is much simplerto solve and which leads to further insights.

A preferred method for calculating the coefficients of the linear filteris based on an MMSE-DFE equalizer. It is shown in the following that thefeedforward filter of a fractionally-spaced MMSE-DFE equalizer isequivalent to the desired linear filter. The solution can be written inclosed form. The computational complexity is much less than solving thecorresponding eigenvalue problem. The filter design gives insight withrespect to impulse response truncation.

The proposed solution generalizes the known receiver published in [12],which does not consider interference cancellation and which applies asymbol-spaced feedforward filter.

In order to derive the filter coefficients, there is considered thefractionally-spaced MMSE-DFE shown in FIG. 5. The task of thefractionally-spaced feedforward filter is, besides interferencesuppression, to truncate the overall impulse response. After suitablefiltering, effectively L′+1 channel coefficients remain. L′ of theremaining channel coefficients are eliminated by the symbol-spacedfeedback filter. Hence, the non-linear detector attached to thefeedforward filter shall be able to handle L′+1 channel coefficients.For example, a Viterbi detector with K=M^(L′) states or relatedtechniques may be applied. The feedback filter and the memory-lessdetector featured in FIG. 5 do not have to be implemented actually. Theyare for conceptional purposes only and may be substituted by anynon-linear equalizer.

If correct decisions are assumed, the feedforward filter and thefeedback filter of the MMSE-DFE are adapted so that the cost function$\begin{matrix}\begin{matrix}{C:={E\left\{ {{{z^{\prime}\lbrack k\rbrack} - {\alpha\left\lbrack {k - k_{0}} \right\rbrack}}}^{2} \right\}}} \\{= {E\left\{ {{{{\overset{\sim}{w}}^{H}y} - {{\overset{\sim}{h}}_{w}^{H}a} - {\alpha\left\lbrack {k - k_{0}} \right\rbrack}}}^{2} \right\}}}\end{matrix} & (10)\end{matrix}$is minimized with respect to {tilde over (w)} and {tilde over (h)}_(w).The vectors {tilde over (w)} and y are the same as defined above. In(10), the data sequence is defined as a=[a[k−k₀−1], . . . ,a[k−k₀−L′]]^(T), and the overall channel coefficients of the desireduser are defined as {tilde over (h)}_(w)=[{tilde over (h)}_(1,w), {tildeover (h)}_(2,w), . . . , {tilde over (h)}_(L′,w)]^(T). The parameter k₀is the decision delay and L′ is the effective memory length of theoverall channel impulse response of the desired user. k₀ and L′ aredesign parameters. Note that the MMSE design criterion is the same asmaximizing the SINR as considered above, if the overall impulse responseis monic, cf. (3) and (10).

The Wirtinger partial derivatives, which minimize the cost function C,are given as follows: $\begin{matrix}{\frac{\partial C}{\partial\overset{\sim}{w}} = {{0^{T}{\quad\quad}{and}\quad\frac{\partial C}{\partial{\overset{\sim}{h}}_{w}}} = {0^{T}.}}} & (11)\end{matrix}$

Assuming i.i.d. data, after some calculation the following relations areobtained:ŵ ^(H) R _(yy) =ĥ _(w) ^(H) R _(ya) +r _(ya) ^(H) and  (12)ĥ _(w) ^(H) =ŵ ^(H) R _(ya) ^(H).  (13)

As defined before, R_(yy):=E{yy^(H)} denotes the 2(L_(w)+1)×2(L_(w)+1)auto-correlation matrix of the received samples y^((i))(t) andR_(ya):=E{ay^(H)} denotes the L′×2(L_(w)+1) cross-correlation matrixbetween the data a^((i)) and the received samples y^((i))(t), and the2(L_(w)+1) vector is defined as r_(ya)=[r_(ya)[k₀], r_(ya)[k₀−1], . . ., r_(ya)[k₀−2L_(w)]]^(T):=E{a*[k−k₀]y}. Upon insertion of (13) in (12)the optimum filter coefficients are obtained asŵ ^(H) =r _(ya) ^(H) [R _(yy) −R _(ya) ^(H) R _(ya)]⁻¹.  (14)

The corresponding channel coefficient vector h is obtained by inserting(14) into (13). Hence, in the proposed solution the filter coefficientsare computed prior to the channel coefficients. The symbol-spacedchannel coefficients ĥ_(w) are provided to the decoupled non-lineardetector. According to (13), (17) and (18), the overall channelcoefficients ĥ_(w) can be written as a convolution between the filtercoefficients, ŵ, and the channel coefficients, h.

The solution discussed so far is suitable for complex symbol alphabetssuch as 8-PSK (PSK: Phase Shift Keying). For one dimensional symbolalphabets such as M-PAM (M-ary-Pulse Amplitude Modulation) or derogatedGMSK (Gaussian Minimum Shift Keying), the performance can be improved byapplying real-valued processing as proposed in [11].

According to a first case (A) there is performed a calculation ofcorrelation matrices in case of available channel estimates.

In the conventional solution as well as in the proposed preferredsolution, in particular the auto-correlation and cross-correlationmatrices R_(yy):=E{yy^(H)} and R_(ya):=E{ay^(H)} have to be computed foreach burst. This computation can be done in a straightforward manner bytaking the expected values E{yy^(H)} and E{ay^(H)} without applying anychannel knowledge.

However, in case of i.i.d. data there can be observed a significantperformance improvement compared to using a short training sequence ifchannel estimates are used. According to the first case, we assume thatchannel estimates for the desired user as well as for the interferer areavailable. This assumption is suitable for synchronous TDMA networkse.g. using defined training sequences. According to a second case, weassume that channel estimates are only available for the desired user, ascenario that is more suitable for asynchronous TDMA networks. Accordingto a third case, we assume that no channel estimates are available forthe filter design.

Consider the auto-correlation matrix R_(yy) defined as $\begin{matrix}\begin{matrix}{R_{yy}:={E\left\{ {yy}^{H} \right\}}} \\{= {\begin{bmatrix}{r_{yy}\left( {0,0} \right)} & {r_{yy}\left( {0,1} \right)} & \cdots & {r_{yy}\left( {0,{{2L_{w}} + 1}} \right)} \\{r_{yy}\left( {1,0} \right)} & {r_{yy}\left( {1,1} \right)} & \cdots & {r_{yy}\left( {1,{{2L_{w}} + 1}} \right)} \\\vdots & \vdots & \cdots & \vdots \\{r_{yy}\left( {{{2L_{w}} + 1},0} \right)} & {r_{yy}\left( {{{2L_{w}} + 1},1} \right)} & \cdots & {r_{yy}\left( {{{2L_{w}} + 1},{{2L_{w}} + 1}} \right)}\end{bmatrix}.}}\end{matrix} & (15)\end{matrix}$

In case of i.i.d data, the elements of the 2(L_(w)+1)×2(L_(w)+1)auto-correlation matrix can be calculated as follows: $\begin{matrix}{{r_{yy}\left( {i,j} \right)} = \left\{ \begin{matrix}{{{\sum\limits_{l = 0}^{L}{h_{l}^{(1)}\left( h_{l + i - j}^{(1)} \right)}^{*}} + {\sum\limits_{m = 1}^{J}{\sum\limits_{l = 0}^{L}{g_{m,l}^{(1)}\left( g_{m,{l + i - j}}^{(1)} \right)}^{*}}} + {\sigma_{n}^{2}\delta_{j - i}}},{0 \leq i},{j \leq L_{w}}} \\{{{\sum\limits_{l = 0}^{L}{h_{l}^{(1)}\left( h_{l + i - j}^{(2)} \right)}^{*}} + {\sum\limits_{m = 1}^{J}{\sum\limits_{l = 0}^{L}{g_{m,l}^{(1)}\left( g_{m,{l + i - j}}^{(2)} \right)}^{*}}} + {\sigma_{n}^{2}\delta_{j - i}}},{0 \leq i \leq L_{w}},{{L_{w} + 1} \leq j \leq {{2L_{w}} + 1}}} \\{{{\sum\limits_{l = 0}^{L}{h_{l}^{(2)}\left( h_{l + i - j}^{(1)} \right)}^{*}} + {\sum\limits_{m = 1}^{J}{\sum\limits_{l = 0}^{L}{g_{m,l}^{(2)}\left( g_{m,{l + i - j}}^{(1)} \right)}^{*}}} + {\sigma_{n}^{2}\delta_{j - i}}},{{L_{w} + 1} \leq i \leq {{2L_{w}} + 1}},{0 \leq j \leq L_{w}}} \\{{{\sum\limits_{l = 0}^{L}{h_{l}^{(2)}\left( h_{l + i - j}^{(2)} \right)}^{*}} + {\sum\limits_{m = 1}^{J}{\sum\limits_{l = 0}^{L}{g_{m,l}^{(2)}\left( g_{m,{l + i - j}}^{(2)} \right)}^{*}}} + {\sigma_{n}^{2}\delta_{j - i}}},{{L_{w} + 1} \leq i},{j \leq {{2L_{w}} + 1}}}\end{matrix} \right.} & (16)\end{matrix}$where σ_(n) ² is the noise variance. Correspondingly, the elementsr_(ya)(i,j) of the L′×2(L_(w)+1) cross-correlation matrix$\begin{matrix}{{R_{ya}:{E\left\{ {ay}^{H} \right\}}} = \begin{bmatrix}{r_{ya}\left( {1,0} \right)} & {r_{ya}\left( {1,1} \right)} & \cdots & {r_{ya}\left( {1,{{2L_{w}} + 1}} \right)} \\{r_{ya}\left( {2,0} \right)} & {r_{ya}\left( {2,1} \right)} & \cdots & {r_{ya}\left( {2,{{2L_{w}} + 1}} \right)} \\\vdots & \vdots & \cdots & \vdots \\{r_{ya}\left( {L^{\prime},0} \right)} & {r_{ya}\left( {L^{\prime},1} \right)} & \cdots & {r_{ya}\left( {L^{\prime},{{2L_{w}} + 1}} \right)}\end{bmatrix}} & (17)\end{matrix}$can be calculated as $\begin{matrix}{{r_{ya}\left( {i,j} \right)} = \left\{ {\begin{matrix}{\left( h_{k_{0} + 1 - j}^{(1)} \right)^{*},{1 \leq i \leq L^{\prime}},{0 \leq j \leq L_{w}}} \\{\left( h_{k_{0} + 1 - j}^{(2)} \right)^{*},{1 \leq i \leq L^{\prime}},{{L_{w} + 1} \leq j \leq {{2L_{w}} + 1}}}\end{matrix}.} \right.} & (18)\end{matrix}$

Finally, the elements of the cross-correlation vector r_(ya) can becomputed as $\begin{matrix}{{r_{ya}(i)} = \left\{ {\begin{matrix}{h_{k_{0} - i}^{(1)},{0 \leq i \leq L_{w}}} \\{h_{k_{0} - i}^{(2)},{{L_{w} + 1} \leq i \leq {{2L_{w}} + 1}}}\end{matrix}.} \right.} & (19)\end{matrix}$

Note that for each burst estimates of h and g_(j), 1≦j≦J and an estimateof the noise variance σ_(n) ² have to be known at the receiver. Thedegradation is small, however, if a constant noise variance, e.g. σ_(n)²=0.001, is assumed. Though an explicit knowledge of the data is notnecessary, training sequences are helpful in order to compute thechannel estimates.

Second case (B) regards to a calculation of correlation matrices in casethat channel estimates are not available for the interferer.

Particularly in asynchronous TDMA networks, an estimation of the channelcoefficients of the interferer is difficult. In case that estimates ofthe channel coefficients of the desired user are available, butestimates of the channel coefficients of the interferer are notavailable, the auto-correlation matrix R_(yy) can be approximated asfollows:

The main idea is to partition R_(yy) in the formR _(yy) =E{y _(d) y _(d) ^(H) }+E{y _(1+n) y _(1+n) ^(H) }=R _(yy) ^(d)+R _(yy) ^(i+n),  (20)where y_(d) ^((i))[k], $\begin{matrix}{{{y_{d}^{(i)}\lbrack k\rbrack} = {\sum\limits_{l = 0}^{L}{{h_{l}^{(i)}\lbrack k\rbrack}{a\left\lbrack {k - l} \right\rbrack}}}},} & (21)\end{matrix}$corresponds to the desired user and y_(i+n) ^((i))[k] $\begin{matrix}{{y_{i + n}^{(i)}\lbrack k\rbrack} = {{\sum\limits_{m = 1}^{J}{\sum\limits_{l = 0}^{L}{{g_{m,l}^{(i)}\lbrack k\rbrack}{b_{m}\left\lbrack {k - l} \right\rbrack}}}} + {n^{(i)}\lbrack k\rbrack}}} & (22)\end{matrix}$corresponds to the interferer plus noise in case of two interferersiε={1,2}. The elements of the auto-correlation matrix R_(yy) ^(d) candetermined for several possible cases and can be written as$\begin{matrix}{{r_{yy}^{d}\left( {i,j} \right)} = \left\{ {\begin{matrix}{{\sum\limits_{l = 0}^{L}{h_{l}^{(1)}\left( h_{l + 1 - j}^{(1)} \right)}^{*}},{0 \leq i},{j \leq L_{w}}} \\{{\sum\limits_{l = 0}^{L}{h_{l}^{(1)}\left( h_{l + {1i} - j}^{(2)} \right)}^{*}},{0 \leq i \leq L_{w}},{{L_{w} + 1} \leq j \leq {{2L_{w}} + 1}}} \\{{\sum\limits_{l = 0}^{L}{h_{l}^{(2)}\left( h_{l + i - j}^{(1)} \right)}^{*}},{{L_{w} + 1} \leq i \leq {{2L_{w}} + 1}},{0 \leq j \leq L_{w}}} \\{{\sum\limits_{l = 0}^{L}{h_{l}^{(2)}\left( h_{l + i - j}^{(2)} \right)}^{*}},{{L_{w} + 1} \leq i},{j \leq {{2L_{w}} + 1}}}\end{matrix}.} \right.} & (23)\end{matrix}$

The elements of the auto-correlation matrix R_(yy) ^(i+n) can beapproximated correspondingly as $\begin{matrix}{{r_{yy}^{i + n}\left( {i,j} \right)} \approx \left\{ {\begin{matrix}{{\frac{1}{K^{\prime}}{\sum\limits_{k = k_{0}}^{K^{\prime} + k_{0} - 1}{{y_{i + n}^{(1)}\left\lbrack {k - i} \right\rbrack}\left( {y_{i + n}^{(1)}\left\lbrack {k - j} \right\rbrack} \right)^{*}}}},{0 \leq i},{j \leq L_{w}}} \\{{\frac{1}{K^{\prime}}{\sum\limits_{k = k_{0}}^{K^{\prime} + k_{0} - 1}{{y_{i + n}^{(1)}\left\lbrack {k - i} \right\rbrack}\left( {y_{i + n}^{(2)}\left\lbrack {k - j} \right\rbrack} \right)^{*}}}},\begin{matrix}{{0 \leq i \leq L_{w}},{L_{w} +}} \\{1 \leq j \leq {{2L_{w}} + 1}}\end{matrix}} \\{{\frac{1}{K^{\prime}}{\sum\limits_{k = k_{0}}^{K^{\prime} + k_{0} - 1}{{y_{i + n}^{(2)}\left\lbrack {k - i} \right\rbrack}\left( {y_{i + n}^{(1)}\left\lbrack {k - j} \right\rbrack} \right)^{*}}}},\begin{matrix}{{{L_{w} + 1} \leq i \leq {{2L_{w}} + 1}},} \\{0 \leq j \leq L_{w}}\end{matrix}} \\{{\frac{1}{K^{\prime}}{\sum\limits_{k = k_{0}}^{K^{\prime} + k_{0} - 1}{{y_{i + n}^{(2)}\left\lbrack {k - i} \right\rbrack}\left( {y_{i + n}^{(2)}\left\lbrack {k - j} \right\rbrack} \right)^{*}}}},{{L_{w} + 1} \leq i},{j \leq {{2L_{w}} + 1}}}\end{matrix},} \right.} & (24)\end{matrix}$where y_(1+n) ^((i))[k]:=y^((i))[k]−y_(d) ^((i))[k]. The parameter K′ isthe length of the training sequence if training symbols are used inorder to calculate R_(yy) ^(i+n). If tentative decisions are used inorder to calculate R_(ya) ^(i+n), the parameter K′ is the burst length.Note that no additional noise variance estimation is needed.

Third case (C) regards to a calculation of correlation matrices in casethat no channel estimates are available.

In the case that co-channel interference is strong, channel estimatesfor the desired user are poor in synchronous and asynchronous networks.Also, channel estimates for the interferer are difficult to obtain inasynchronous networks. For these reasons, the following novel solutionis proposed, where no channel estimation and no estimation of the noisevariance is necessary for the design of the linear filter. In a firststep, there are computed the filter coefficientsŵ ^(H) =r _(ya) ^(H) [R _(yy) −R _(ya) ^(H) R _(ya)]⁻¹,  (25)where R_(yy), R_(ya) and r_(ya) ^(H) are computed as follows.

Again, the elements of the auto-correlation matrix can be approximatedas $\begin{matrix}{{r_{yy}\left( {i,j} \right)} \approx \left\{ \begin{matrix}{{\frac{1}{K^{\prime}}{\sum\limits_{{k = k_{0}}\quad}^{K^{\prime} + k_{0} - 1}{{y^{(1)}\left\lbrack {k - i} \right\rbrack}\left( {y^{(1)}\left\lbrack {k - j} \right\rbrack} \right)^{*}}}},{0 \leq i},{j \leq L_{w}}} \\{{\frac{1}{K^{\prime}}{\sum\limits_{k = k_{0}}^{K^{\prime} + k_{0} - 1}{{y^{(1)}\left\lbrack {k - i} \right\rbrack}\left( {y^{(2)}\left\lbrack {k - j} \right\rbrack} \right)^{*}}}},\begin{matrix}{0 \leq i \leq {L_{w} +}} \\{1 \leq j \leq {{2L_{w}} + 1}}\end{matrix}} \\{{\frac{1}{K^{\prime}}{\sum\limits_{k = k_{0}}^{K^{\prime} + k_{0} - 1}{{y^{(2)}\left\lbrack {k - i} \right\rbrack}\left( {y^{(1)}\left\lbrack {k - j} \right\rbrack} \right)^{*}}}},\begin{matrix}{{{L_{w} + 1} \leq i \leq {{2L_{w}} + 1}},} \\{0 \leq j \leq L_{w}}\end{matrix}} \\{{\frac{1}{K^{\prime}}{\sum\limits_{k = k_{0}}^{K^{\prime} + k_{0} - 1}{{y^{(2)}\left\lbrack {k - i} \right\rbrack}\left( {y^{(2)}\left\lbrack {k - j} \right\rbrack} \right)^{*}}}},{{L_{w} + 1} \leq i},{j \leq {{2L_{w}} + 1}}}\end{matrix} \right.} & (26)\end{matrix}$

A parameter K′ is the length of the training sequence if trainingsymbols are used in order to calculate R_(ya). If tentative decisionsare used in order to calculate R_(ya), the parameter K′ is the burstlength.

Correspondingly, the elements of the cross-correlation matrix R_(ya) canbe approximated as $\begin{matrix}{{r_{ya}\left( {i,j} \right)} \approx \left\{ {\begin{matrix}{{\frac{1}{K^{\prime}}{\sum\limits_{k = k_{0}}^{K^{\prime} + k_{0} - 1}{{a\left\lbrack {k - k_{0} - i} \right\rbrack}\left( {y^{(1)}\left\lbrack {k - j} \right\rbrack} \right)^{*}}}},\begin{matrix}{{1 \leq i \leq L^{\prime}},} \\{0 \leq j \leq L_{w}}\end{matrix}} \\{{\frac{1}{K^{\prime}}{\sum\limits_{k = k_{0}}^{K^{\prime} + k_{0} - 1}{{a\left\lbrack {k - k_{0} - i} \right\rbrack}\left( {y^{(2)}\left\lbrack {k - j} \right\rbrack} \right)^{*}}}},\begin{matrix}{{1 \leq i \leq L^{\prime}},{L_{w} +}} \\{1 \leq j \leq {{2L_{w}} + 1}}\end{matrix}}\end{matrix}.} \right.} & (27)\end{matrix}$

Finally, the elements of the cross-correlation vector r_(ya) can beapproximated as $\begin{matrix}{{r_{ya}(i)} \approx \left\{ \begin{matrix}{{\frac{1}{K^{\prime}}{\sum\limits_{k = k_{0}}^{K^{\prime} + k_{0} - 1}{{a\left\lbrack {k - k_{0}} \right\rbrack}\left( {y^{(1)}\left\lbrack {k - i} \right\rbrack} \right)^{*}}}},{0 \leq i \leq L_{w}}} \\{{\frac{1}{K^{\prime}}{\sum\limits_{k = k_{0}}^{K^{\prime} + k_{0} - 1}{{a\left\lbrack {k - k_{0}} \right\rbrack}\left( {y^{(2)}\left\lbrack {k - i} \right\rbrack} \right)^{*}}}},{{L_{w} + 1} \leq i \leq {{2L_{w}} + 1}}}\end{matrix} \right.} & (28)\end{matrix}$

In a second step, the convolutiony _(w) :=w ^(H) y  (29)is performed.

In a third step, a conventional LS channel estimation is done giveny_(w) and a. Due to the interference suppression, the channel estimationerror is reasonably small. Finally, MLSE of the desired user is donegiven y_(w) and the estimated channel coefficients.

For the numerical results presented in FIG. 6-FIG. 11, the followinggeneral scenarios are assumed as synchronous or asynchronous GSMnetwork, having J=1 dominant interferer, having uniformly distributedtraining sequence code (TSC) for desired user and interferer, having TSCof dominant interferer differs from TSC of desired user, this can bemanaged by the network operator, using TU0 & TU50 channel modelaccording to GSM, setting L=3, using a decoupled MMSE filter/Viterbidetector, said proposed receiver being compatible with the conventionalGSM receiver, and using real-valued processing in MMSE filter.

In FIG. 6 and FIG. 7 L _(w)=11 corresponds to 48 real-valued filtercoefficients. In FIG. 8 to FIG. 11 L _(w)=4 corresponds to 20real-valued filter coefficients. Further, L′=L=3, i.e. no truncation hasbeen used, i.e., M^(L′)=8 state Viterbi detector.

FIG. 6 and FIG. 7 illustrate calculation of correlation matricesaccording first case A, FIG. 8 and FIG. 9 illustrate second case B, andFIG. 10 and FIG. 11 illustrate third case C. Further, there has beenused statistically independent bursts. There is illustrated the BitError Rate of the signal components of the desired user versus C/I indB.

Not filled spots and boxes show values according to performing signalprocessing according to preferred embodiment. As a reference,performance results for the conventional receiver are included as wellas black filled spots and boxes. The performance/complexity trade-off isquite remarkable. Particularly, note that in FIG. 10 and FIG. 11 nochannel estimation is used.

Numerical results shown for the popular GSM system illuminatesignificant performance improvements. The performance may be furtherimproved (i) by iterative processing and (ii) by introducing a weightingfactor ρ, which is a function of the SINR, in conjunction withR _(yy) =R _(yy) ^(d) +ρR _(yy) ^(i+n)  (30)in order to avoid performance degradation compared to the conventionalreceiver at high SINR. The weighting factor may be obtained by atable-look-up. Although oversampling is assumed, degradation in case ofa symbol-spaced implementation is not serious.

When determining, i.e. calculating the auto-correlation matrix it ispossible to use knowledge of at least one desired channel transmittingdesired data from first base station BS1 and using knowledge of at leastone disturbing, especially interfering channel transmitting disturbingdata from e.g. second and third base stations BS2, BS3 said knowledgebeing determined by channel estimation in the decision-feedbackequalizer DFE. According to second case B, calculation of theauto-correlation matrix is done using only knowledge of at least onedesired channel transmitting desired data. According to third case C,calculation of the auto-correlation matrix is done using neitherknowledge of a desired channel transmitting desired data nor usingknowledge of a disturbing, especially interfering channel transmittingdisturbing data.

When designing the filter coefficients for pre-filtering there can beconsidered only one or a plurality of receiving antennas. Further, thefilter coefficients can consider data received according to the timingof the symbols or can consider oversampling. The filter coefficients canbe designed as real or complex coefficients. Further, the filtercoefficients for pre-filtering can be improved by an iterative method.

In addition, it is possible to estimate the SINR for controlling thedesign of the filter coefficients to avoid a degradation compared with aconventional receiver in case of high SINR.

Further, it is possible to process the output signal of the pre-filterprocess i.e. the pre-filtered data z using a memory-less detector, usinga backwardly directed filter of a MMSE-DFE equalizer together with amemory-less detector, or using a non-linear equalizer, e.g. a Trellistree or graph based equalizer.

Especially, it is preferred to use channel coefficients, especiallycoefficients of channel impulse response, of a non-linear equalizercorresponding to the coefficients of an MMSE-DFE backwardly directedfilter.

Advantageously, the channel coefficients of the non-linear equalizer canbe determined or calculated by convolution of the coefficients of thepre-filter with the originally estimated channel coefficients of thedesired signal. Further, it is possible to estimate the channelcoefficient of the non-linear equalizer from the pre-filtered receivedsignal with aid of a channel estimator.

Summarized, a decoupled linear filter/non-linear detector was developed.The purpose of the fractionally-spaced linear filter is to suppress CCIand to shorten the overall impulse response. The purpose of thenon-linear detector is ISI rejection for the desired user. The receiverstructure is compatible with conventional TDMA receivers.

The proposed solution generalizes the known receiver published in [12].Particular emphasis is on adaptation techniques for the linear filtertaking channel knowledge of the desired user and, optionally, of theinterferer into account. Also, the case of no channel estimation for thepurpose of designing the linear filter is considered.

1-15. (canceled)
 16. A method for interference cancellation of datareceived via a communication channel in a cellular communication systemhaving corresponding channel coefficients, the method comprising thesteps of: performing linear space-time filtering of the received data;performing non-linear detection via signal processing to get detecteddata; and determining linear space-time filter coefficients for linearpre-filtering of the received data, and wherein the filter coefficientsare used to suppress non-Gaussian interference during filtering of thereceived data.
 17. The method according to claim 16, further comprisingthe steps of: determining optimized channel coefficients with respect toa maximum value of a signal-to interference/noise ratio; and using theoptimized channel coefficients as an input to the non-linear detectionto get detected data.
 18. The method according to claim 18, wherein theoptimized channel coefficients are determined by a convolution betweensaid linear space-time filter coefficients and the channel coefficientsfor a desired user.
 19. The method according to claim 18, wherein theoptimized channel coefficients are determined by a channel estimatorthat uses pre-filtered data.
 20. The method according to claim 16,further comprising the step of determining the optimized channelcoefficients for a non-linear equalizer by convolution of the filtercoefficients using estimated channel coefficients of desired data. 21.The method according to claim 16, further comprising the step ofestimating optimized channel coefficients for non-linear equalizationusing a pre-filtered signal and using channel estimation.
 22. The methodaccording to claim 16, further comprising the steps of processing of thepre-filtered data using a memory-less detector, using a backwardlydirected filter of a Minimum Mean Square Error-Decision-FeedbackEqualizer (MMSE-DFE) in connection with a memory-less detector.
 23. Themethod according to claim 16, further comprising the steps of processingof the pre-filtered data using a non-linear equalizer.
 24. Acommunication device for communicating to users in a cellularcommunication network, comprising: a receiver unit that receives datasent from another device over a communication channel and receives datasent from at least one further device via an interfering channel,wherein the channels have corresponding channel coefficients in relationto a desired user; at least one processing unit that first performslinear space-time filtering of the received data, and subsequentlyperforms non-linear detection via signal processing to get detecteddata; and further determines linear space-time filter coefficients forlinear pre-filtering of the received data, wherein the filtercoefficients are used to suppress non-Gaussian interference duringfiltering of the received data
 25. The communication device according toclaim 24, wherein one of the at least one processing unit is a linearpre-filter for suppressing non-Gaussian interference, followed by anon-linear detector to cancel intersymbol interference.